Everybody’s got to learn sometime? A causal machine learning evaluation of training programmes for jobseekers in France

Héloïse Burlat

Labour Economics, 2024, vol. 89, issue C

Abstract: This paper estimates the heterogeneous impact of three types of vocational training- preparation, qualifying, and combined – on jobseekers’ return to employment using the Modified Causal Forest method. Analysing data from 33,699 individuals over 24 months, it reveals a short-term negative lock-in effect for all programmes, persisting in the medium term for combined training. Only qualifying training shows a positive medium-term effect. Seniors, low-skilled, foreign-born, and those with poor job histories benefit most, while youth and higher education levels benefit less. Targeting foreign-born individuals could significantly enhance programme effectiveness, as indicated by the clustering analysis and optimal policy trees.

Keywords: Policy evaluation; Active labour market policy; Continuing vocational training; Causal machine learning; Causal forest; Conditional average treatment effects (search for similar items in EconPapers)
JEL-codes: C21 J08 J24 J68 (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:labeco:v:89:y:2024:i:c:s092753712400068x

DOI: 10.1016/j.labeco.2024.102573

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